The Internet is an excellent resource for searching for information. Over the years, many different search engines have been designed to provide users with easy ways to search for data. Recent improvements in search engines include predictive searching, where the search engine predicts the user's search term based on what previous users have searched for that start with the same terms.
But the organization of the data returned as a result of a search is not an area that is well developed. Search engines return the data organized by what seems most likely to be desired by the user. While a reasonable strategy when the search term is fairly specific, this strategy does not work so well when the search terms are broad (either intentionally or accidentally). For example, a search for the term “Paris” could result in information relating to Paris, France (the city), Paris Hotel (the casino in Las Vegas, N.V.), or Paris Hilton (the socialite). Because it is likely that different users searching for the term “Paris” have intended all three targets, the results of a search for the term “Paris” will have results for all three targets intermixed. Thus, a user interested only in Paris, France would need to manually skip over entries relating to the casino and the socialite from the results. The situation becomes even more complicated when the user might be interested in combinations of the information: for example, information about the last time Paris Hilton stayed at the Paris Hotel. And even knowing how the data is grouped does not tell a person anything about why the data is grouped the way it is.
A need remains for a way to address these and other problems associated with the prior art.